Redundancy optimization for critical components in high-availability technical systems

We consider a user who buys a number of identical technical systems (e.g., medical, manufacturing, or communication systems) for which she must have very high availability. In such a situation, there are typically several options that the user can choose to facilitate this availability: cold standby redundancy for critical components, buying spare parts with the systems so failed parts can be replaced quickly, and/or application of an emergency procedure to expedite repairs when there is a stock out. To these options we introduce another: the possibility of initiating an emergency shipment when stock is one. Thus, the user may choose different combinations of the redundancy decision and the timing of applications of the emergency procedure, as well as how much spare parts inventory to purchase. We formulate the problem as the minimization of the total costs—acquisition, spare parts, and repair—incurred for the systems over their lifetimes, under a constraint for the total uptime of all systems. We optimally solve the problem by decomposing the multicomponent problem into single-component problems and then conducting exact analysis on these single-component problems. Using these, we construct an efficient frontier that reflects the trade-off between the uptime and the total costs of the systems. In addition, we provide a method to rank the components by the relative value of investing in redundancy. We illustrate these results through numerical example

Redundancy optimization for critical components in high-availability capital goods

We consider a user who buys a number of identical capital goods systems (e.g., medical, manufacturing, or communication systems) for which she must have very high availability. In such a situation, there are typically several options that can be used to facilitate this availability. Often, the user can choose to build in cold standby redundancy for critical components. She may also typically buy spare parts with the systems so that during their exploitation phase, when a part in a system fails, the failed part can be replaced by a ready-for-use part from inventory. In addition, an emergency procedure is usually available by which a part is shipped from a distant central warehouse (at an additional cost) to be applied when there is a stock out. To these options we introduce another: The possibility of initiating an emergency shipment when stock is one. Thus, the user may choose one of three policies per component: The different combinations of the redundancy decision and the timing of applications of the emergency procedure. (In addition, she must decide how much spare parts inventory to purchase, for any policy.) Each policy provides different total uptime against different total costs incurred. We formulate the problem as the minimization of the total costs incurred for the systems over their lifetimes, under a constraint for the total uptime of all systems. These total costs consist of acquisition costs, spare parts costs, and repair costs. We optimally solve the problem by decomposing the multi-component problem into single-component problems, and then conducting exact analysis on these single-component problems, we derive results on when each of the three policies is optimal. Using these, we construct an efficient frontier which reflects the trade-off between the uptime and the total costs of the systems. In addition, we provide a method to rank the components by the relative value of investing in redundancy. We illustrate these results through numerical examples

Optimization of component reliability in the design phase of capital goods

We introduce a quantitative model to support the decision on the reliability level of a critical component during its design. We consider an OEM who is responsible for the availability of its systems in the field through service contracts. Upon a failure of a critical part in a system during the exploitation phase, the failed part is replaced by a ready-for-use part from a spare parts inventory. In an out-of-stock situation, a costly emergency procedure is applied. The reliability levels and spare parts inventory levels of the critical components are the two main factors that determine the downtime and corresponding costs of the systems. These two levels are decision variables in our model. We formulate the portions of Life Cycle Costs (LCC) which are affected by a component’s reliability and its spare parts inventory level. These costs consist of design costs, production costs, and maintenance and downtime costs in the exploitation phase. We conduct exact analysis and provide an efficient optimization algorithm. We provide managerial insights through a numerical experiment which is based on real-life data

Life cycle costs measurement of complex systems manufactured by an engineer-to-order company

Complex technical systems such as packaging lines, computer networks, material handling systems, are crucial for the operations at the companies (or institutions) where they are installed. Companies require high availability because their primary processes may halt when these systems are down. High availability implies a high level of support and service activities, and, thus high support and service costs in the exploitation phase. Together with acquisition costs, operational costs and maybe some others, support and service costs constitute the Life Cycle Costs (LCC) of a system. Then the question is what portion of LCC consists of support and service costs? If this portion is high, the companies should take into account these costs more carefully in their decisions. In this work, we develop a Life Cycle Costs Measurement (LCCM) methodology by adapting a Life Cycle Costs Analysis (LCCA) approach. With this methodology, cost buckets that compose LCC of the systems are determined and costs are measured for each bucket. LCC is just the sum of those costs then. This methodology is used to measure the LCC of complex systems at two different sites. These systems are manufactured by an Engineer-To-Order (ETO) company that we have cooperated with. The company designs and engineers systems from existing building blocks such that the customer requirements are met. We mainly focus on the measurement of the cost buckets that stem from the exploitation phase of these systems. The cost figures show that the nominal support and service costs are more than the acquisition costs and account for a significant portion of LCC. This suggests that both the suppliers and the buyers of these systems should deal with support and service costs seriously, when investments in new systems are made

Developing an economical model for reliability allocation of an electro-optical system by considering reliability improvement difficulty, criticality, and subsystems dependency

Abstract The nature of electro-optical equipment in various industries and the pursuit of the goal of reducing costs demand high reliability on the part of electro-optical systems. In this respect, reliability improvement could be addressed through a reliability allocation problem. Subsystem reliability must be increased such that the requirements as well as defined requisite functions are ensured in accordance with the designers’ opinion. This study is an attempt to develop a multi-objective model by maximizing system reliability and minimizing costs in order to investigate design phase costs as well as production phase costs. To investigate reliability improvement feasibility in the design phase, effective feasibility factors in the system are used and the sigma level index is incorporated in the production phase as the reliability improvement difficulty factor. Thus, subsystem reliability improvement priorities are taken into consideration. Subsystem dependency degree is investigated through the design structure matrix and incorporated into the model’s limitation together with modified criticality. The primary model is converted into a single-objective model through goal programming. This model is implemented on electro-optical systems, and the results are analyzed. In this method, reliability allocation follows two steps. First, based on the allocation weights, a range is determined for the reliability of subsystems. Afterward, improvement is initiated based upon the costs and priorities of subsystem reliability improvement